Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
Results
Showing 1 to 20 of 134 for “"Theory and Algorithms"”.
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Adaptable optimization : theory and algorithms
… is a central ingredient for analyzing and designing systems with incomplete information. This thesis addresses uncertainty in optimization, in a dynamic framework where information is revealed sequentially, and future decisions are adaptable, i.e., they depend functionally on the …
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Theory and algorithms for swept manifold intersections
… aided geometric design, geometric modeling, and computational topology have generated a spate of interest towards geometric objects called swept volumes. Besides their great applicability in various practical areas, the mere geometry and topology of these entities make them a perfect testbed …
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Stochastic shortest path games : theory and algorithms
… of Technology, Dept. of Electrical Engineering and Computer Science, 1997.
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Proof theory and algorithms for answer set programming
… ASP faces a growing range of applications, demanding for high-performance tools able to solve complex problems. ASP integrates ideas from a variety of neighboring fields. In particular, automated techniques to search for answer sets are inspired by Boolean Satisfiability (SAT) solving …
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Nearly reducible finite Markov chains: theory and algorithms
… sciences, biological sciences, economics, and elsewhere. Markov chains that appear in realistic modelling tasks are frequently observed to be nearly reducible, incorporating a mixture of fast and slow processes that leads to ill-conditioning of the underlying matrix of probabilities for …
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Stochastic and shortest path games : theory and algorithms
… of Technology, Dept. of Electrical Engineering and Computer Science, 1997.
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Learning in High Dimensional Spaces: Applications, Theory, and Algorithms
… results are used to extend the existing learning algorithms. Based on the results from probabilistic classifiers, we have proposed an improved learning algorithm for HMMs which attempts to learn a maximum likelihood classifier under the minimum conditional entropy prior. A margin distribution …
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Theory and Algorithms for Nonlinear Optimization and Variational Inequalities
… in nonlinear optimization, one theoretical topic and two algorithmic topics. Each of these topics deals with a broad class of nonlinear optimization problems. We first introduce and analyze a generalized parametric variational inequality problem $PVI(E, T, C, \psi$, Z) in locally convex Hausdorff …
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New variational principles with applications to optimization theory and algorithms
… of variational analysis in optimization theory and algorithms. In the first part we develop new extremal principles in variational analysis that deal with finite and infinite systems of convex and nonconvex sets. The results obtained, under the name of tangential extremal principles and …
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Online and active learning of big networks: theory and algorithms
… the Internet Age, in which information entities and objects are interconnected, thereby forming gigantic information networks. These networks are not only massive, but also grow and evolve very quickly. It is critical to quickly process and understand these networks in order to enable data-driven …
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Bayesian regularization for graphical models and variants: Theory and algorithms
This Dissertation was approved for publication on 2019-04-19 at 09:57.
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Theory and Algorithms for Penalization, Graphical Models, and Surrogate Marker Evaluation
… oracle inequality in high-dimensional statistics theory, graphical models, and surrogate measures in clinical trials. First, we introduce a general slow rate bound for maximum regularized likelihood estimators in Kullback-Leibler divergence. The result applies to a wide variety of models and …
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New Theory and Algorithms for Convex Optimization with Non-Standard Structures
Optimization models and algorithms have long played central and indispensable roles in the advancement of science and engineering. In recent years, first-order methods have played important roles in tackling applications arising in machine learning and data science, due to their simplicity, …
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Multisensor Signal Processing: Theory and Algorithms for Image -Based Rendering and Multichannel Sampling
… filters, slow A/D converters, digital expanders, and digital synthesis filters to approximate a fast A/D converter. The synthesis filters are to be designed to minimize the maximum gain of an induced error system. We show the equivalence of this system to a digital system, used to design …
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Interpretation, Identification and Reuse of Models. Theory and algorithms with applications in predictive toxicology.
… applications that offer an environment to build and store predictive models. Unfortunately, they do not provide advanced functionalities that allow for efficient model selection and for interpretation of model predictions for new data. This thesis aims to address these issues and proposes …
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Interpretation, Identification and Reuse of Models. Theory and algorithms with applications in predictive toxicology.
… applications that offer an environment to build and store predictive models. Unfortunately, they do not provide advanced functionalities that allow for efficient model selection and for interpretation of model predictions for new data. This thesis aims to address these issues and proposes …
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Computational Methods for Hybrid Systems
… computational approaches for the analysis and synthesis of nonlinear systems. Each of these approaches is presented in a conference paper that describes parts of the theory and algorithms. Matlab commands implementing the algorithms have been developed and the manuals for these are included …
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Graphical Models for Video Understanding
… processing techniques, approximate methods, and online learning. I will demonstrate how the theory and algorithms usefully apply to the variety of tasks ranging from video clustering and stabilization, to video retrieval and building of the similarity measures between the distributions.
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On the Complexity of Scheduling University Courses
… rarely are precise problem definitions provided and no papers were found which offered proofs that the university course scheduling problem being discussed is NP-Complete. This thesis defines a scheduling problem that has realistic constraints. It schedules professors to sections of courses they …
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